U.S. patent application number 16/622393 was filed with the patent office on 2020-07-30 for methods and systems for processing an unltrasound image.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to MAN NGUYEN, JEAN-LUC FRANCOIS-MARIE ROBERT, JUN SEOB SHIN, FRANCOIS GUY GERARD MARIE VIGNON.
Application Number | 20200237345 16/622393 |
Document ID | 20200237345 / US20200237345 |
Family ID | 1000004777033 |
Filed Date | 2020-07-30 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200237345 |
Kind Code |
A1 |
SHIN; JUN SEOB ; et
al. |
July 30, 2020 |
METHODS AND SYSTEMS FOR PROCESSING AN UNLTRASOUND IMAGE
Abstract
The invention provides methods and systems for generating an
ultrasound image. In a method, the generation of an ultrasound
image comprises: obtaining channel data, the channel data defining
a set of imaged points; for each imaged point: isolating the
channel data; performing a spectral estimation on the isolated
channel data; and selectively attenuating the spectral estimation
channel data, thereby generating filtered channel data; and summing
the filtered channel data, thereby forming a filtered ultrasound
image. In some examples, the method comprises aperture
extrapolation. The aperture extrapolation improves the lateral
resolution of the ultrasound image. In other examples, the method
comprises transmit extrapolation. The transmit extrapolation
improves the contrast of the image. In addition, the transmit
extrapolation improves the frame rate and reduces the motion
artifacts in the ultrasound image. In further examples, the
aperture and transmit extrapolations may be combined.
Inventors: |
SHIN; JUN SEOB; (MEDFORD,
MA) ; VIGNON; FRANCOIS GUY GERARD MARIE; (ANDOVER,
MA) ; NGUYEN; MAN; (MELROSE, MA) ; ROBERT;
JEAN-LUC FRANCOIS-MARIE; (CAMBRIDGE, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
EINDHOVEN |
|
NL |
|
|
Family ID: |
1000004777033 |
Appl. No.: |
16/622393 |
Filed: |
June 11, 2018 |
PCT Filed: |
June 11, 2018 |
PCT NO: |
PCT/EP2018/065258 |
371 Date: |
December 13, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62520233 |
Jun 15, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 8/4461 20130101;
A61B 8/5207 20130101; A61B 8/4494 20130101; G01S 7/52046 20130101;
G01S 15/8997 20130101 |
International
Class: |
A61B 8/08 20060101
A61B008/08; A61B 8/00 20060101 A61B008/00; G01S 15/89 20060101
G01S015/89; G01S 7/52 20060101 G01S007/52 |
Claims
1. A method for generating an ultrasound image, the method
comprising: obtaining beam-summed data using an ultrasonic probe,
wherein the beam-summed data is formed from a plurality of steering
angles; for each steering angle of the beam-summed data, segmenting
the beam-summed data based on an axial imaging depth of the
beam-summed data; for each segment of the segmented beam-summed
data: estimating an extrapolation filter, based on the segmented
beam-summed data, the extrapolation filter having a filter order;
and extrapolating, by an extrapolation factor, the segmented
beam-summed data based on the extrapolation filter, thereby
generating extrapolated beam-summed data; and coherently
compounding the extrapolated beam-summed data across all segments,
thereby generating the ultrasound image.
2. A method as claimed in claim 1, the method further comprising:
for each axial segment of the beam-summed data, applying a Fourier
transform to this axial segment of the beam-summed data; and
performing an inverse Fourier transform on the extrapolated
beam-summed data.
3. A method as claimed in claim 1, wherein the beam-summed data is
obtained by way of at least one of plane wave imaging and diverging
wave imaging.
4. A method as claimed in claim 1, wherein the axial segment is
less than 4 wavelengths of a transmit signal that provides the
beam-summed data in depth, for example less than or equal to 2
wavelengths in depth.
5. A method as claimed in claim 1, wherein the plurality of
steering angles comprises less fewer than 20 angles, for examples
less than or equal to 10 angles.
6. A method as claimed in claim 1, wherein the filter order is less
than or equal to half the number of steering angles.
7. A method as claimed in claim 1, wherein the extrapolation factor
is less than or equal to 10, for example less than or equal to
8.
8. A method as claimed in claim 1, wherein the estimation of the
extrapolation filter is performed using an autoregressive
model.
9. A method as claimed in claim 1, wherein the estimation of the
extrapolation filter is performed using the Burg technique.
10. A method as claimed in claim 1, wherein the extrapolation
occurs in an axial direction of the beam-summed data.
11. A computer program comprising computer program code means which
is adapted, when said computer program is run on a computer, to
implement the method of claim 1.
12. A controller for controlling the generation of an ultrasound
image, wherein the controller is adapted to: obtain beam-summed
data by way of an ultrasonic probe, wherein the beam-summed data is
formed from a plurality of steering angles; for each steering angle
of the beam-summed data, segment the beam-summed data based on an
axial depth of the beam-summed data; for each segment of the
segmented beam-summed data: estimate an extrapolation filter, based
on the segmented beam-summed data, the extrapolation filter having
a filter order; and extrapolate, by an extrapolation factor, the
segmented beam-summed data based on the extrapolation filter,
thereby generating extrapolated beam-summed data; and coherently
compound the extrapolated beam-summed data across all segments,
thereby generating the ultrasound image.
13. An ultrasound system, the system comprising: an ultrasonic
probe; a controller as claimed in claim 12; and a display device
for displaying the high contrast ultrasound image.
14. A system as claimed in claim 13, wherein the system further
comprises a user interface having a user input.
15. A system as claimed in claim 14, wherein the user input is
adapted to adjust at least one of: the axial depth of the axial
segment; the extrapolation factor; and the filter order.
Description
RELATED APPLICATION
[0001] This application claims the benefit of and priority to U.S.
Provisional Application No. 62/520,233, filed Jun. 15, 2017, which
is incorporated by reference in its entirety.
FIELD OF THE INVENTION
[0002] This invention relates to the field of ultrasound imaging,
and more specifically to the field of ultrasound image
filtering.
BACKGROUND OF THE INVENTION
[0003] Ultrasound imaging is increasingly being employed in a
variety of different applications. It is important that the image
produced by the ultrasound system is as clear and accurate as
possible so as to give the user a realistic interpretation of the
subject being scanned. This is especially the case when the subject
in question is a patient undergoing a medical ultrasound scan. In
this situation, the ability of a doctor to make an accurate
diagnosis is dependent on the quality of the image produced by the
ultrasound system.
[0004] Off-axis clutter is a significant cause of image degradation
in ultrasound. Adaptive beamforming techniques, such as minimum
variance (MV) beamforming, have been developed and applied to
ultrasound imaging to achieve an improvement in image quality;
however, MV beamforming is computationally intensive as an
inversion of the spatial covariance matrix is required for each
pixel of the image. In addition, even though MV beamforming is
developed primarily for an improvement in spatial resolution, and
is not ideal for reducing off-axis clutter, its performance in
terms of improving spatial resolution often needs to be sacrificed
by reducing the subarray size. Otherwise, image artifacts may occur
in the speckle due to signal cancellation.
[0005] Adaptive weighting techniques, such as: the coherence factor
(CF); the generalized coherence factor (GCF); the phase coherence
factor (PCF); and the short-lag spatial coherence (SLSC), have been
proposed but all require access to per-channel data to compute a
weighting mask to be applied to the image. Further, these methods
would only work for conventional imaging with focused transmit
beams and are not suitable for plane wave imaging (PWI) or
diverging wave imaging (DWI) involving only a few transmits.
[0006] In addition, spatial resolution in ultrasound images,
particularly the lateral resolution, is often suboptimal. The -6 dB
lateral beamwidth at the focal depth is determined by the following
equation:
- 6 dB Beamwidth lateral = .lamda. z D ##EQU00001##
where .lamda. is wavelength, z is transmit focal depth, and D is
the aperture size. The smaller the wavelength (or the higher center
frequency), the better the lateral resolution will be; however, a
smaller wavelength is achieved at the cost of penetration depth. On
the other hand, a larger aperture size D is needed to achieve a
better lateral resolution; however, the aperture size is often
limited by the human anatomy, hardware considerations, and system
cost.
[0007] Adaptive beamforming techniques, such as the previously
mentioned minimum variance (MV) beamforming, have been a topic of
active research. These methods are data-dependent beamforming
methods that seek to adaptively estimate the apodization function
that yields lateral resolution beyond the diffraction limit.
[0008] In addition to standard ultrasound imaging techniques, plane
wave imaging (PWI) or diverging wave imaging (DWI) in the case of
phased arrays is a relatively new imaging technique, which has the
potential to perform imaging at very high frame rates above 1 kHz
and possibly several kHz. These techniques have also opened up many
new possible imaging modes for different applications which were
previously not possible with conventional focused transmit beams.
For this reason, they have been some of the most actively
researched topics in academia in recent years.
[0009] PWI/DWI can achieve high frame rates by coherently
compounding images obtained from broad transmit beams at different
angles. Since the spatial resolution in PWI/DWI is generally known
to be only slightly worse than, or comparable to, conventional
imaging with focused transmit beams, its main drawback is
degradation in the image contrast, which is directly related to the
number of transmit angles. The image contrast is generally low for
a small number of transmit angles in PWI/DWI; therefore, many
transmit angles are needed to maintain image quality comparable to
that of conventional imaging with focused transmit beams.
[0010] PWI/DWI also suffers from motion artifacts, particularly
when imaging fast-moving organs such as the heart, as individual
image pixels are constructed from signals from different transmit
beams. The effect of motion becomes more severe with increasing
numbers of transmit angles; therefore, the dilemma in PWI/DWI
systems is clear: more transmit angles are required to achieve high
image contrast, but also result in more motion artifacts that
degrade image quality.
[0011] Further, regardless of the number of transmit angles,
PWI/DWI does not reduce reverberation clutter, which is one of the
main sources of image quality degradation in fundamental B-mode
ultrasound images.
SUMMARY OF THE INVENTION
[0012] The present invention provides systems and methods capable
of performing off-axis clutter filtering, improving spatial
resolution, and improving image contrast. In certain aspects, the
present inventions proposes an extrapolation technique of the
transmitted plane waves based on a linear prediction scheme, which
allows for an extremely high frame rate while significantly
improving image contrast. An additional benefit of the invention is
that these benefits may be accomplished without significantly
increasing the computational burden of the ultrasound system.
[0013] According to examples in accordance with an aspect of the
invention, there is provided a method for performing off-axis
clutter filtering in an ultrasound image, the method
comprising:
[0014] obtaining channel data from an ultrasonic probe defining a
set of imaged points, wherein the ultrasonic probe comprises an
array of transducer elements;
[0015] for each imaged point in a region of interest: [0016]
isolating the channel data associated with said imaged point;
[0017] performing a spectral estimation of the isolated channel
data; and [0018] selectively attenuating the isolated channel data
by way of an attenuation coefficient based on the spectral
estimation, thereby generating filtered channel data; and
[0019] summing the filtered channel data, thereby generating a
filtered ultrasound image.
[0020] This method performs off-axis clutter filtering in an
ultrasound image. By performing a spectral estimation of the
channel data it is possible to identify the frequency content of
the channel data. Typically, off-axis clutter signal will possess a
high spatial frequency, which may be identified in the spectral
estimation of the channel data. In this way, it is possible to
selectively attenuate signals having high spatial frequencies,
thereby reducing and/or eliminating off-axis clutter signals in the
final ultrasound image.
[0021] In an embodiment, the isolating of the channel data
comprises processing a plurality of observations of the imaged
point.
[0022] In this way, it is possible to perform the spectral
estimation on channel data that has been averaged over a plurality
of measurements. In this way, the accuracy of the channel data, and
so of the final ultrasound image, is increased.
[0023] In an arrangement, the spectral estimation comprises
decomposing the channel data into a finite sum of complex
exponentials.
[0024] By decomposing the channel data into a finite sum of complex
exponentials, it is simple to identify the components with high
spatial frequencies, thereby making it easier to attenuate the
off-axis clutter signals in the ultrasound image.
[0025] In a further arrangement, the complex exponentials
comprise:
[0026] a first model parameter; and
[0027] a second model parameter.
[0028] In a further embodiment, the first model parameter is
complex.
[0029] In a yet further embodiment, the second model parameter is
inversely proportional to the distance between adjacent transducer
elements of the ultrasonic probe.
[0030] The first and second model parameters may be used to
describe the nature of the channel data. In the case that the first
model parameter is complex, the imaginary component relates to the
phase of the signals and the modulus, which may be a real, positive
number, relates to the amplitude of the signal. The second model
parameter may relate to the spatial frequency of the signal.
[0031] In a still yet further arrangement, the first and second
model parameters are estimated by way of spectral estimation.
[0032] In some designs, the attenuation coefficient is
Gaussian.
[0033] In this way, it is simple to implement attenuation for
signals approaching higher spatial frequencies. The aggressiveness
of the filtering may be tuned by altering the width of the Gaussian
used.
[0034] In an embodiment, the attenuation coefficient is depth
dependent.
[0035] By making the attenuation coefficient depth dependent, it is
possible to control the amount of off-axis clutter signal filtering
for different depths.
[0036] In an arrangement, the attenuation coefficient is dependent
on the second model parameter.
[0037] In this way, the attenuation coefficient may be directly
dependent on the spatial frequency of the signal, meaning that the
attenuation coefficient may adapt to the signals on an individual
basis rather than requiring an input from the user.
[0038] In some embodiments, the attenuation coefficient is adapted
to attenuate the channel data to half the width of the receive
beampattern.
[0039] In this way, it is possible to both improve the lateral
resolution and decrease the off-axis clutter in the filtered
ultrasound image.
[0040] In some arrangements, the spectral estimation is based on an
autoregressive model.
[0041] According to examples in accordance with a further aspect of
the invention, there is provided a computer program comprising
computer program code means which is adapted, when said computer
program is run on a computer, to implement the method described
above.
[0042] According to examples in accordance with a further aspect of
the invention, there is provided a controller for controlling the
filtering of off-axis clutter in an ultrasound image, wherein the
controller is adapted to:
[0043] obtain channel data from an ultrasonic probe defining a set
of imaged points;
[0044] for each imaged point in a region of interest: [0045]
isolate the channel data associated with said imaged point; [0046]
perform a spectral estimation of the isolated channel data; and
[0047] selectively attenuate the isolated channel data by way of an
attenuation coefficient based on the spectral estimation, thereby
generating filtered channel data; and
[0048] sum the filtered channel data, thereby generating a filtered
ultrasound image.
[0049] According to examples in accordance with a further aspect of
the invention, there is provided an ultrasound system, the system
comprising:
[0050] an ultrasonic probe, the ultrasonic probe comprising an
array of transducer elements;
[0051] a controller as defined above; and
[0052] a display device for displaying the filtered ultrasound
image.
[0053] According to examples in accordance with a further aspect of
the invention, there is provided a method for generating an
ultrasound image, the method comprising:
[0054] obtaining channel data by way of an ultrasonic probe;
[0055] for each channel of the channel data, segmenting the channel
data based on an axial imaging depth of the channel data;
[0056] for each segment of the segmented channel data: [0057]
estimating an extrapolation filter based on the segmented channel
data, the extrapolation filter having a filter order; and [0058]
extrapolating, by an extrapolation factor, the segmented channel
data based on the extrapolation filter, thereby generating
extrapolated channel data; and
[0059] summing the extrapolated channel data across all segments,
thereby generating the ultrasound image.
[0060] This method performs aperture extrapolation on the channel
data, thereby increasing the lateral resolution of the ultrasound
image. By estimating the extrapolation filter based on the
segmented channel data, the extrapolation filter may directly
correspond to the channel data. In this way, the accuracy of the
extrapolation performed on the segmented channel data is increased.
In other words, this method predicts channel data from transducer
elements that do not physically exist within the ultrasonic probe
by extrapolating the existing channel data.
[0061] In an embodiment, the method further comprises:
[0062] for each axial segment of the channel data, applying a
Fourier transform to an axial segment of the channel data; and
[0063] performing an inverse Fourier transform on the extrapolated
channel data.
[0064] By performing the extrapolation of the segmented channel
data in the temporal frequency domain, the accuracy of the
extrapolated channel data may be further increased.
[0065] In an arrangement, the axial segment is less than 4
wavelengths in depth, for example less than or equal to 2
wavelengths in depth.
[0066] In this way, the performance of the system, which is largely
dependent on the number of segments of channel data to be
extrapolated, may be improved whilst maintaining the improvement in
lateral resolution of the image, which is inversely proportional to
the size of the axial segment.
[0067] In an embodiment, the extrapolation factor is less than or
equal to 10.times., for example less than or equal to 8.times..
[0068] In this way, it is possible to achieve a substantial
improvement in the lateral resolution of the ultrasound image
whilst preserving the speckle texture within the image.
[0069] In some designs, the estimation of the extrapolation filter
is performed using an autoregressive model.
[0070] In an arrangement, the estimation of the extrapolation
filter is performed using the Burg technique.
[0071] In this way, the extrapolation filter may be simply
estimated without requiring a significant amount of processing
power.
[0072] In some embodiments, the filter order is less than or equal
to 5, for example less than or equal to 4.
[0073] In this way, it is possible to achieve a substantial
improvement in the lateral resolution of the ultrasound image
whilst preserving the speckle texture within the image.
[0074] In an embodiment, the extrapolation occurs in the azimuthal
direction in the aperture domain.
[0075] According to examples in accordance with a further aspect of
the invention, there is provided a computer program comprising
computer program code means which is adapted, when said computer
program is run on a computer, to implement the method defined
above.
[0076] According to examples in accordance with a further aspect of
the invention, there is provided a controller for controlling the
generation of an ultrasound image, wherein the controller is
adapted to:
[0077] obtain channel data by way of an ultrasonic probe;
[0078] for each channel of the channel data, segment the channel
data based on an axial imaging depth of the channel data;
[0079] for each segment of the segmented channel data: [0080]
estimate an extrapolation filter based on the segmented channel
data, the extrapolation filter having a filter order; and [0081]
extrapolate, by an extrapolation factor, the segmented channel data
based on the extrapolation filter, thereby generating extrapolated
channel data; and
[0082] sum the extrapolated channel data across all segments,
thereby generating the ultrasound image.
[0083] According to examples in accordance with a further aspect of
the invention, there is provided an ultrasound system, the system
comprising:
[0084] an ultrasonic probe;
[0085] a controller as defined above; and
[0086] a display device for displaying the ultrasound image.
[0087] In an embodiment, the system further comprises a user
interface having a user input.
[0088] In this way, it is possible for a user to provide an
instruction to the ultrasound system.
[0089] In an arrangement, the user input is adapted to adjust the
axial depth of the axial segment.
[0090] In a further arrangement, the user input is adapted to alter
the extrapolation factor.
[0091] In a yet further arrangement, the user input is adapted to
alter the filter order.
[0092] In this way, the user may empirically adapt the various
parameters of the extrapolation method in order to maximize the
image quality according to their subjective opinion.
[0093] According to examples in accordance with a yet aspect of the
invention, there is provided a method for generating an ultrasound
image, the method comprising:
[0094] obtaining beam-summed data by way of an ultrasonic probe,
wherein the beam-summed data comprises a plurality of steering
angles;
[0095] for each steering angle of the beam-summed data, segmenting
the beam-summed data based on an axial imaging depth of the channel
data;
[0096] for each segment of the segmented beam-summed data: [0097]
estimating an extrapolation filter, based on the segmented
beam-summed data, the extrapolation filter having a filter order;
and [0098] extrapolating, by an extrapolation factor, the segmented
beam-summed data based on the extrapolation filter, thereby
generating extrapolated beam-summed data; and
[0099] coherently compounding the extrapolated beam-summed data
across all segments, thereby generating the ultrasound image.
[0100] This method performs transmit extrapolation on the
beam-summed data, thereby improving the contrast of the ultrasound
image. The beam-sum data corresponds to data summed across the
aperture for several transmit beams overlapping on a point of
interest. In addition, this method provides an increase in
ultrasound image frame rate and a reduction in motion artifacts in
the final ultrasound image by retaining contrast and resolution
with fewer actual transmit events. By estimating the extrapolation
filter based on the segmented beam-summed data, the extrapolation
filter may directly correspond to the beam-summed data. In this
way, the accuracy of the extrapolation performed on the segmented
beam-summed data is increased. In other words, this method predicts
beam-summed data from transmit angles outside of the range of
angles used to obtain the original beam-summed data by
extrapolation.
[0101] In an embodiment, the method further comprises:
[0102] for each axial segment of the beam-summed data, applying a
Fourier transform to an axial segment of the beam-summed data;
and
[0103] performing an inverse Fourier transform on the extrapolated
beam-summed data.
[0104] By performing the extrapolation of the segmented beam-summed
data in the temporal frequency domain, the accuracy of the
extrapolated beam-summed data may be further increased.
[0105] In an arrangement, the beam-summed data is obtained by way
of at least one of plane wave imaging and diverging wave
imaging.
[0106] In this way, it is possible to produce an ultrafast
ultrasound imaging method with increased image contrast and frame
rate.
[0107] In some embodiments, the axial segment is less than 4
wavelengths in depth, for example less than or equal to 2
wavelengths in depth.
[0108] In this way, the performance of the system, which is largely
dependent on the number of segments of beam-summed data to be
extrapolated, may be improved whilst maintaining the improvement in
image quality, which is inversely proportional to the size of the
axial segment.
[0109] In some arrangements, the plurality of steering angles
comprises less than 20 angles, for examples less than or equal to
10 angles.
[0110] In this way, the computational performance of the ultrasound
system may be improved, as there are fewer steering angle to
process, whilst maintaining the detail of the final ultrasound
image, which is proportional to the number of steering angle
used.
[0111] In some designs, the filter order is less than or equal to
half the number of steering angles.
[0112] In an embodiment, the extrapolation factor is less than or
equal to 10.times., for example less than or equal to 8.times..
[0113] In this way, it is possible to achieve a substantial
improvement in the contrast resolution of the ultrasound image
whilst preserving the speckle texture within the image.
[0114] In an arrangement, the estimation of the extrapolation
filter is performed using an autoregressive model.
[0115] In an embodiment, the estimation of the extrapolation filter
is performed using the Burg technique.
[0116] In this way, the extrapolation filter may be simply
estimated without requiring a significant amount of processing
power.
[0117] In an arrangement, the extrapolation occurs in the transmit
direction.
[0118] According to examples in accordance with a further aspect of
the invention, there is provided a computer program comprising
computer program code means which is adapted, when said computer
program is run on a computer, to implement the method defined
above.
[0119] According to examples in accordance with a further aspect of
the invention, there is provided a controller for controlling the
generation of an ultrasound image, wherein the controller is
adapted to:
[0120] obtain beam-summed data by way of an ultrasonic probe,
wherein the beam-summed data comprises a plurality of steering
angles;
[0121] for each steering angle of the beam-summed data, segment the
beam-summed data based on an axial depth of the beam-summed
data;
[0122] for each segment of the segmented beam-summed data: [0123]
estimate an extrapolation filter, based on the segmented
beam-summed data, the extrapolation filter having a filter order;
and [0124] extrapolate, by an extrapolation factor, the segmented
beam-summed data based on the extrapolation filter, thereby
generating extrapolated beam-summed data; and
[0125] coherently compound the extrapolated beam-summed data across
all segments, thereby generating the ultrasound image.
[0126] According to examples in accordance with a further aspect of
the invention, there is provided an ultrasound system, the system
comprising:
[0127] an ultrasonic probe;
[0128] a controller as defined above; and
[0129] a display device for displaying the high contrast ultrasound
image.
[0130] In an embodiment, the system further comprises a user
interface having a user input.
[0131] In this way, it is possible for a user to provide an
instruction to the ultrasound system.
[0132] In a further embodiment, the user input is adapted to adjust
at least one of: the axial depth of the axial segment; the
extrapolation factor; and the filter order.
[0133] In this way, the user may empirically adapt the various
parameters of the extrapolation method in order to maximize the
image quality according to their subjective opinion.
BRIEF DESCRIPTION OF THE DRAWINGS
[0134] Examples of the invention will now be described in detail
with reference to the accompanying drawings, in which:
[0135] FIG. 1 shows an ultrasound diagnostic imaging system to
explain the general operation;
[0136] FIG. 2 shows a method of selectively attenuating channel
data of an ultrasound image;
[0137] FIG. 3 shows an illustration of the implementation of the
method of FIG. 2;
[0138] FIG. 4 shows a method of performing aperture extrapolation
on an ultrasound image;
[0139] FIG. 5 shows an illustration of the method of FIG. 4;
[0140] FIG. 6, FIG. 7, FIG. 8 and FIG. 9 show examples of the
implementation of the method of FIG. 4;
[0141] FIG. 10 shows a method of performing transmit extrapolation
on an ultrasound image;
[0142] FIG. 11 shows an illustration of the method of FIG. 10;
and
[0143] FIG. 12, FIG. 13, and FIG. 14 show examples of the
implementation of the method of FIG. 10.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0144] The invention provides methods and systems for generating an
ultrasound image. In a method, the generation of an ultrasound
image comprises: obtaining channel data, the channel data defining
a set of imaged points; for each imaged point: isolating the
channel data; performing a spectral estimation on the isolated
channel data; and selectively attenuating the spectral estimation
channel data, thereby generating filtered channel data; and summing
the filtered channel data, thereby forming a filtered ultrasound
image.
[0145] In some examples, the method comprises aperture
extrapolation. The aperture extrapolation improves the lateral
resolution of the ultrasound image. In other examples, the method
comprises transmit extrapolation. The transmit extrapolation
improves the contrast of the image. In addition, the transmit
extrapolation improves the frame rate and reduces the motion
artifacts in the ultrasound image. In further examples, the
aperture and transmit extrapolations may be combined.
[0146] The general operation of an exemplary ultrasound diagnostic
imaging system will first be described, with reference to FIG. 1,
and with emphasis on the signal processing function of the system
since this invention relates to the processing of the signals
measured by the transducer array.
[0147] The system comprises an array transducer probe 10 which has
a CMUT transducer array 100 for transmitting ultrasound waves and
receiving echo information. The transducer array 100 may
alternatively comprise piezoelectric transducers formed of
materials such as PZT or PVDF. The transducer array 100 is a
two-dimensional array of transducers 110 capable of scanning in a
2D plane or in three dimensions for 3D imaging. In another example,
the transducer array may be a 1D array.
[0148] The transducer array 100 is coupled to a microbeamformer 12
in the probe which controls reception of signals by the CMUT array
cells or piezoelectric elements. Microbeamformers are capable of at
least partial beamforming of the signals received by sub-arrays (or
"groups" or "patches") of transducers as described in U.S. Pat. No.
5,997,479 (Savord et al.), U.S. Pat. No. 6,013,032 (Savord), and
U.S. Pat. No. 6,623,432 (Powers et al.).
[0149] Note that the microbeamformer is entirely optional. The
examples below assume no analog beamforming.
[0150] The microbeamformer 12 is coupled by the probe cable to a
transmit/receive (T/R) switch 16 which switches between
transmission and reception and protects the main beamformer 20 from
high energy transmit signals when a microbeamformer is not used and
the transducer array is operated directly by the main system
beamformer. The transmission of ultrasound beams from the
transducer array 10 is directed by a transducer controller 18
coupled to the microbeamformer by the T/R switch 16 and a main
transmission beamformer (not shown), which receives input from the
user's operation of the user interface or control panel 38.
[0151] One of the functions controlled by the transducer controller
18 is the direction in which beams are steered and focused. Beams
may be steered straight ahead from (orthogonal to) the transducer
array, or at different angles for a wider field of view. The
transducer controller 18 can be coupled to control a DC bias
control 45 for the CMUT array. The DC bias control 45 sets DC bias
voltage(s) that are applied to the CMUT cells.
[0152] In the reception channel, partially beamformed signals are
produced by the microbeamformer 12 and are coupled to a main
receive beamformer 20 where the partially beamformed signals from
individual patches of transducers are combined into a fully
beamformed signal. For example, the main beamformer 20 may have 128
channels, each of which receives a partially beamformed signal from
a patch of dozens or hundreds of CMUT transducer cells or
piezoelectric elements. In this way the signals received by
thousands of transducers of a transducer array can contribute
efficiently to a single beamformed signal.
[0153] The beamformed reception signals are coupled to a signal
processor 22. The signal processor 22 can process the received echo
signals in various ways, such as band-pass filtering, decimation, I
and Q component separation, and harmonic signal separation which
acts to separate linear and nonlinear signals so as to enable the
identification of nonlinear (higher harmonics of the fundamental
frequency) echo signals returned from tissue and micro-bubbles. The
signal processor may also perform additional signal enhancement
such as speckle reduction, signal compounding, and noise
elimination. The band-pass filter in the signal processor can be a
tracking filter, with its pass band sliding from a higher frequency
band to a lower frequency band as echo signals are received from
increasing depths, thereby rejecting the noise at higher
frequencies from greater depths where these frequencies are devoid
of anatomical information.
[0154] The beamformers for transmission and for reception are
implemented in different hardware and can have different functions.
Of course, the receiver beamformer is designed to take into account
the characteristics of the transmission beamformer. In FIG. 1 only
the receiver beamformers 12, 20 are shown, for simplicity. In the
complete system, there will also be a transmission chain with a
transmission micro beamformer, and a main transmission
beamformer.
[0155] The function of the micro beamformer 12 is to provide an
initial combination of signals in order to decrease the number of
analog signal paths. This is typically performed in the analog
domain.
[0156] The final beamforming is done in the main beamformer 20 and
is typically after digitization.
[0157] The transmission and reception channels use the same
transducer array 10' which has a fixed frequency band. However, the
bandwidth that the transmission pulses occupy can vary depending on
the transmission beamforming that has been used. The reception
channel can capture the whole transducer bandwidth (which is the
classic approach) or by using bandpass processing it can extract
only the bandwidth that contains the useful information (e.g. the
harmonics of the main harmonic).
[0158] The processed signals are coupled to a B mode (i.e.
brightness mode, or 2D imaging mode) processor 26 and a Doppler
processor 28. The B mode processor 26 employs detection of an
amplitude of the received ultrasound signal for the imaging of
structures in the body such as the tissue of organs and vessels in
the body. B mode images of structure of the body may be formed in
either the harmonic image mode or the fundamental image mode or a
combination of both as described in U.S. Pat. No. 6,283,919
(Roundhill et al.) and U.S. Pat. No. 6,458,083 (Jago et al.) The
Doppler processor 28 processes temporally distinct signals from
tissue movement and blood flow for the detection of the motion of
substances such as the flow of blood cells in the image field. The
Doppler processor 28 typically includes a wall filter with
parameters which may be set to pass and/or reject echoes returned
from selected types of materials in the body.
[0159] The structural and motion signals produced by the B mode and
Doppler processors are coupled to a scan converter 32 and a
multi-planar reformatter 44. The scan converter 32 arranges the
echo signals in the spatial relationship from which they were
received in a desired image format. For instance, the scan
converter may arrange the echo signal into a two dimensional (2D)
sector-shaped format, or a pyramidal three dimensional (3D) image.
The scan converter can overlay a B mode structural image with
colors corresponding to motion at points in the image field with
their Doppler-estimated velocities to produce a color Doppler image
which depicts the motion of tissue and blood flow in the image
field. The multi-planar reformatter will convert echoes which are
received from points in a common plane in a volumetric region of
the body into an ultrasound image of that plane, as described in
U.S. Pat. No. 6,443,896 (Detmer). A volume renderer 42 converts the
echo signals of a 3D data set into a projected 3D image as viewed
from a given reference point as described in U.S. Pat. No.
6,530,885 (Entrekin et al.).
[0160] The 2D or 3D images are coupled from the scan converter 32,
multi-planar reformatter 44, and volume renderer 42 to an image
processor 30 for further enhancement, buffering and temporary
storage for display on an image display 40. In addition to being
used for imaging, the blood flow values produced by the Doppler
processor 28 and tissue structure information produced by the B
mode processor 26 are coupled to a quantification processor 34. The
quantification processor produces measures of different flow
conditions such as the volume rate of blood flow as well as
structural measurements such as the sizes of organs and gestational
age. The quantification processor may receive input from the user
control panel 38, such as the point in the anatomy of an image
where a measurement is to be made. Output data from the
quantification processor is coupled to a graphics processor 36 for
the reproduction of measurement graphics and values with the image
on the display 40, and for audio output from the display device 40.
The graphics processor 36 can also generate graphic overlays for
display with the ultrasound images. These graphic overlays can
contain standard identifying information such as patient name, date
and time of the image, imaging parameters, and the like. For these
purposes the graphics processor receives input from the user
interface 38, such as patient name. The user interface is also
coupled to the transmit controller 18 to control the generation of
ultrasound signals from the transducer array 10' and hence the
images produced by the transducer array and the ultrasound system.
The transmit control function of the controller 18 is only one of
the functions performed. The controller 18 also takes account of
the mode of operation (given by the user) and the corresponding
required transmitter configuration and band-pass configuration in
the receiver analog to digital converter. The controller 18 can be
a state machine with fixed states.
[0161] The user interface is also coupled to the multi-planar
reformatter 44 for selection and control of the planes of multiple
multi-planar reformatted (MPR) images which may be used to perform
quantified measures in the image field of the MPR images.
[0162] FIG. 2 shows a method 200 of performing selective
attenuation on an ultrasound image.
[0163] In step 210, channel data is obtained from an ultrasonic
probe. The channel data defines a set of imaged points within a
region of interest.
[0164] In step 220, the channel data is isolated for a given imaged
point, meaning that it may be operated on independently of the
remaining channel data.
[0165] In step 230, a spectral estimation is performed on the
isolated channel data. For example, the channel data may be
decomposed into a finite sum of complex exponentials as
follows:
S(x).apprxeq..SIGMA..sub.i=1.sup.Na.sub.ie.sup.ik.sup.i.sup.x,
[0166] where: x is the lateral coordinate along the array of
transducer elements of the ultrasonic probe; S(x) is the measured
channel data signal at x; N is the model order, which is the number
of sinusoidal components used to describe the channel data; a.sub.i
is the first model parameter; and k.sub.i is the second model
parameter. Any spectral estimation method may be performed on the
isolated channel data. For example, a Fourier transform may be
performed in combination with a Total Variation method. In another
example, a complex L1/L2 minimization may be used to decompose the
channel data signal as a sparse sum of off-axis and off-range
components. In the example above, an autoregressive model is
used.
[0167] In this case, the a.sub.i are complex parameters, wherein
the phase may be between -.pi. and .pi. and wherein the modulus is
a real, positive number, indicative of the strength of the channel
data signal.
[0168] The k.sub.i may also theoretically be complex; however, in
this example they are real numbers. They may theoretically range
from -.infin. to .infin. but in practice, due to sampling
restrictions; they range from
- 1 dx to 1 dx , ##EQU00002##
where dx is me element spacing in the transducer array of the
ultrasonic probe.
[0169] In this example, the first and second model parameters may
be estimated through any known method in the art spectral
estimation techniques. For example, the parameters may be estimated
by way of a non-parametric method, such as a fast Fourier transform
(FFT) or discrete Fourier transform (DFT), or a parametric method,
such as the autoregressive (AR) or autoregressive moving average
(ARMA) methods.
[0170] In step 240, the channel data is selectively attenuated by
including an attenuation coefficient in the above formulation:
S.sub.f(x).apprxeq..SIGMA..sub.i=1.sup.Nw(k.sub.i)a.sub.ie.sup.ik.sup.i.-
sup.x,
[0171] where: S.sub.f(x) is the filtered channel data signal at x;
and w(k.sub.i) is the attenuation coefficient, wherein w(k.sub.i)
may be a real number.ltoreq.1.
[0172] w(k.sub.i) is inversely proportional to k.sub.i, meaning
that at higher spatial frequencies, the value of w(k.sub.i)
decreases, thereby attenuating the high spatial frequency clutter
signals. In other words, the attenuation coefficient may be
dependent on the spatial frequency of the channel data. The
attenuation coefficient is applied across the entire frequency
spectrum of the channel data, thereby attenuating any high spatial
frequency signals included within the channel data. w(k.sub.i) may,
for example, be Gaussian in shape as shown in the following
equation:
w ( k i ) = e - k i 2 / k 0 2 , ##EQU00003##
[0173] where k.sub.0 is an additional parameter, which dictates the
width of the Gaussian and so the aggressiveness of the high spatial
frequency signal attenuation. For lower values of k.sub.0, the
Gaussian is thinner and so the clutter filtering is more
aggressive. For larger values of k.sub.0, the width of the Gaussian
is increased leading to less aggressive clutter filtering, which in
turn allows more signals to contribute to the final ultrasound
image. In this example, the value of k.sub.0 may be selected to be
of the order of magnitude of the inverse of the aperture size, for
example
k 0 = .pi. a , ##EQU00004##
where a is the aperture size. In addition, the value of k.sub.0 may
be altered depending on the axial depth of the current channel data
segment.
[0174] Alternative functions may also be used as an attenuation
coefficient. For example, it may be possible to estimate the
angular transmit, or round-trip, beampattern of the channel data,
such as through simulation, to use as a weighting mask. Further, a
rectangular function may be used, wherein the width of the function
dictates a cutoff frequency above which all signals are rejected.
Further still, an exponential decay function may also be used.
[0175] Steps 220 to 240 are repeated for each axial segment of the
channel data. When the final segment of channel data has undergone
selective attenuation, the method may progress to step 250.
[0176] In step 250, the filtered channel data is summed to form the
final clutter filtered ultrasound image.
[0177] FIG. 3 shows a comparison between an ultrasound image of a
heart at various stages in the method describe above with reference
to FIG. 2.
[0178] The first image 260 shows the original ultrasound image
captured from the raw channel data. As can be seen, the image
contains a high level of noise and the details are difficult to
make out.
[0179] The second image 270 shows the ultrasound image at stage 230
of the method, where the image has been reconstructed with the
sparse sinusoidal decomposition as described above. In this
example, the order of the model, N=4, meaning that the channel data
at each depth is modeled as a sum 4 of sinusoids. The improvement
in image clarity can already be seen; however, the signal is still
noisy and the finer details, particularly towards the top of the
image, remain largely unclear.
[0180] The third image 280 shows the ultrasound image at stage 250
of the method, after the application of the selective attenuation,
thereby eliminating the sinusoidal signals with the highest spatial
frequencies. As can be seen from the image, the signal noise has
been significantly reduced by the attenuation of the high spatial
frequency signals.
[0181] FIG. 4 shows a method 300 for applying aperture
extrapolation to an ultrasound image.
[0182] In step 310, channel data is obtained by way of an
ultrasonic probe.
[0183] In step 320, the channel is segmented based on an axial
imaging depth of the axial data.
[0184] The axial window size of the segmented channel data may be
empirically determined to suit the visual preferences of the user.
For example, an axial window size of in the range of 1-2
wavelengths may produce a preferred image quality improvement. A
larger axial window may provide a more reliable improvement in
image quality and better preservation of speckle texture; however,
it may adversely affect the axial resolution of the ultrasound
image.
[0185] In step 330, an extrapolation filter of order p, a.sub.j
where 1.ltoreq.j.ltoreq.p, is estimated based on the segmented
channel data. In this case, the extrapolation filter may be
estimated by way of the well-known Burg technique for
autoregressive (AR) parameter estimation.
[0186] In step 340, the segmented channel data is extrapolated
using the extrapolation filter estimated in step 330. In this case,
a 1-step linear prediction extrapolator to obtain the 1.sup.st
forward-extrapolated sample X.sub.N+1, using the extrapolation
filter and the previous p samples, as shown below:
X.sub.N+1=.SIGMA..sub.j=1.sup.pX.sub.N+1-ja.sub.j,
[0187] where: X.sub.N is the current sample; X.sub.N+1 is the
forward-extrapolated sample; and p is the order of the
extrapolation filter, a.sub.j.
[0188] The forward extrapolation may be generalized as follows:
X.sub.k=.SIGMA..sub.j=1.sup.pX.sub.k-ja.sub.j, k>N (Forward
Extrapolation),
[0189] where k is the number of forward extrapolations
performed.
[0190] By reversing the filter order and taking the complex
conjugate of the filter coefficients, it is possible to
backward-extrapolate the value up to the k.sup.th channel as a
linear combination of the first p channels:
X.sub.k=.SIGMA..sub.j=1.sup.pX.sub.k+ja*.sub.j, k<N (Backward
Extrapolation),
[0191] Using both the forward and backward extrapolation formulae,
it is possible to fully extrapolate the segmented channel data.
Steps 330 and 340 are repeated for each axial segment of the
segmented channel data.
[0192] In step 350, the fully extrapolated channel data segments
are summed to obtain the beamsum signal and generate the final
ultrasound image.
[0193] FIG. 5 shows an illustration 400 of an embodiment of the
method of FIG. 4.
[0194] In step 410, channel data is obtained by way of an
ultrasonic probe. The plot shows signal intensity, by way of the
shading, for each channel at a given axial depth. In the plots of
steps 410, 440, 450 and 460, the horizontal axis represents the
channel being measured and the vertical axis represents the axial
depth of the measured channel data, wherein the axial depth is
inversely proportional to the height of the vertical axis. In the
plots of steps 420 and 430, the horizontal axis represents the
channel being measured and the vertical axis represents the
temporal frequency of the channel data.
[0195] In step 420, a fast Fourier transform (FFT) is applied to
the channel data, thereby transforming the channel data into the
temporal frequency domain. In this case, the extrapolation filter
is also estimated in the temporal frequency domain. The estimation
is once again performed by the Burg technique.
[0196] In step 430, the estimated extrapolation filter is used to
extrapolate the temporal frequency domain channel data beyond the
available aperture. The extrapolated data is highlighted by the
boxes to the right, representing the forward extrapolated channel
data, and the left, representing the backward extrapolated channel
data, of the plot.
[0197] In step 440, an inverse Fourier transform is applied to the
extrapolated temporal frequency channel data, thereby generating
spatial channel data. As can be seen from the plot, the channel
data now covers a wider aperture than in step 410.
[0198] In step 450, the axial window is moved to a new axial
segment and steps 420 to 440 are repeated.
[0199] In step 460, the fully extrapolated channel data is
obtained. This may then be used to generate the final ultrasound
image.
[0200] FIGS. 6 to 11 show examples of the implementation of
embodiments of the method of FIG. 4.
[0201] FIG. 6 shows a comparison between an ultrasound image before
and after the implementation of two embodiments of the method of
FIG. 4. In FIGS. 6 to 8, the horizontal axes of the images
represent the lateral position, measured in mm, of the signals and
the vertical axes represent the axial position, measured in mm, of
the signals. The gradient scales indicate the signal intensity at a
given signal location.
[0202] The first image 500 shows a conventional delay and sum (DAS)
beamformed image of a simulated phantom. In this case, a 32-element
aperture was used in both transmit and receive steps. As can be
seem from the image, the two simulated cysts introduce heavy
scattering and cause a large amount of noise in the ultrasound
image. This results in the cysts appearing poorly defined and
unclear.
[0203] The second image 510 shows the same ultrasound image after
the application of the aperture extrapolation technique described
above. In this case, the receive aperture was extrapolated by a
factor of 2 using an extrapolation filter of order 4. As can be
seen from the image, the extrapolation has substantially improved
the lateral resolution of the image whilst maintaining the quality
of the speckle texture.
[0204] The third image 520 once again shows the same ultrasound
image after the application of the aperture extrapolation technique
described above; however, in this case, the receive aperture was
extrapolated by a factor of 4 using an extrapolation filter of
order 4. As can be seen from the image, the extrapolation by this
additional factor has further increased the lateral resolution of
the ultrasound image.
[0205] FIGS. 7 and 8 show a comparison between an ultrasound image
before and after the application of an aperture extrapolation
method as described above. The images are shown in the 60 dB
dynamic range.
[0206] In both cases, the top images, 570 and 590, show a
conventional DAS beamformed cardiac image. The bottom images, 580
and 600, show the ultrasound images after an aperture extrapolation
by a factor of 8. In both Figures, it can be seen that the aperture
extrapolation leads to an improvement in both lateral resolution
and image contrast.
[0207] FIG. 9 shows a comparison between an ultrasound image of a
leg before and after the application of an aperture extrapolation
method as described above. The images are shown in the 60 dB
dynamic range.
[0208] The first image 610 shows a conventional DAS beamformed
ultrasound image. The second image 620 shows the same ultrasound
image after the application of an aperture extrapolation method as
described above. The aperture was extrapolated by a factor of 8. As
can be seen from the second image, the aperture extrapolation leads
to an improvement in the lateral resolution of the ultrasound
image.
[0209] FIGS. 6 to 9 show an improvement in lateral resolution due
to the aperture extrapolation method across a wide variety of
imaging scenarios.
[0210] FIG. 10 shows a method 700 for applying transmit
extrapolation to an ultrasound image.
[0211] In step 710, beam-summed data is obtained by way of an
ultrasonic probe, wherein the beam-summed data comprises a
plurality of steering angles.
[0212] In step 720, the beam-summed data is segmented for each
steering angle based on the axial depth of the beam-summed
data.
[0213] In step 730, an extrapolation filter of order p, a.sub.j
where 1.ltoreq.j.ltoreq.p, is estimated based on the segmented
beam-summed data. In this case, the extrapolation filter may be
estimated by way of the well-known Burg technique for
autoregressive (AR) parameter estimation.
[0214] In step 740, the segmented beam-summed data is extrapolated
using the extrapolation filter estimated in step 730. In this case,
a 1-step linear prediction extrapolator to obtain the 1.sup.st
forward-extrapolated sample X.sub.N+1, using the extrapolation
filter and the previous p samples of the available transmit angles,
as shown below:
X.sub.N+1=.SIGMA..sub.j=1.sup.pX.sub.N+1-ja.sub.j,
[0215] where: X.sub.N is the current sample; X.sub.N+1 is the
forward-extrapolated sample; and p is the order of the
extrapolation filter, a.sub.j.
[0216] The forward extrapolation may be generalized as follows:
X.sub.k=.SIGMA..sub.j=1.sup.pX.sub.k-ja.sub.j, k>N (Forward
Extrapolation),
[0217] where k is the number of forward extrapolations
performed.
[0218] By reversing the filter order and taking the complex
conjugate of the filter coefficients, it is possible to
backward-extrapolate the value up to the k.sup.th transmit angle as
a linear combination of the first p transmit angles:
X.sub.k=.SIGMA..sub.j=1.sup.pX.sub.k+ja*.sub.j, k<N (Backward
Extrapolation),
[0219] Using both the forward and backward extrapolation formulae,
it is possible to fully extrapolate the segmented beam-summed data.
Steps 730 and 740 are repeated for each axial segment of the
segmented beam-summed data.
[0220] In step 750, the fully extrapolated beam-summed data
segments are coherently compounded to obtain the final beamsum
signal and generate the final ultrasound image.
[0221] The transmit scheme used in the above method may be: planar;
diverging; single-element; or focused.
[0222] FIG. 11 shows an illustration of the method of FIG. 10. For
each of a plurality of steering angles 752, a low contrast
ultrasound image 754 is obtained. By coherently compounding 756 the
plurality of low contrast ultrasound images, it is possible to
generate a single high contrast ultrasound image 758. The
improvement in image contrast is proportional to the number of
steering angles used, and so the number of low contrast ultrasound
images coherently compounded to generate the high contrast
ultrasound image; however, a large number of initial steering
angles may result in a decreased computational performance of the
ultrasound system. By extrapolating from a small number of initial
steering angles, it is possible to increase the image contrast
without significantly degrading the performance of the ultrasound
system.
[0223] FIG. 12 shows a comparison between an ultrasound image
before and after the implementation of the transmit extrapolation
and a reference image.
[0224] The first image 760 shows a reference image of a simulated
phantom containing a 40 mm diameter anechoic cyst lesion. The
phantom contains 4 strong point scatterers in the speckle
background and 4 weak point scatterers inside of the anechoic
lesion. The image was formed using 24 diverging waves.
[0225] The second image 770 shows an image of the same simulated
phantom formed from only the 6 central angles of the 24 steering
angles of the reference image. The 24 steering angles are evenly
spaced between -45.degree. and 45.degree., meaning that the central
6 steering angles are separated by an angle of 3.91.degree.. It is
clear to see from this image that the reduced number of steering
angles results in a lower image contrast.
[0226] The third image 780 shows the result of applying the
transmit extrapolation described above to the second image 770. In
this case, an extrapolation filter of order 3 was used to
extrapolate the number of transmit angles by a factor of 4. The
image is generated by coherently compounding the data from the
initial 6 transmit angles with the 18 predicted beamsum data from
the extrapolation. As can be seen from the third image, the
contrast is significantly improved from the second image and is
comparable to that of the reference image. In addition, the
contrast enhancement does not result in any artifacts suppressing
the weak point scatterers inside the anechoic lesion.
[0227] FIG. 13 shows a comparison between an ultrasound image
before and after the implementation of the transmit extrapolation
and a reference image.
[0228] The first image 790 shows a reference image of an apical
4-chamber view of the heart form a patient. The image was formed
using 24 diverging waves.
[0229] The second image 800 shows an image of the same data set
formed with only 6 diverging waves. As before, the 6 diverging
waves were selected as the central 6 of the 24 steering angles. As
can be seen from the image, the image contrast of the second image
is significantly lower than that of the first image.
[0230] The third image 810 shows the result of applying a transmit
extrapolation to the second image as described above. In this case,
an extrapolation filter of order 3 was used to extrapolate the
number of steering angles by a factor of 4. Once again, the image
is generated by coherently compounding the data from the initial 6
transmit angles with the 18 predicted beamsum data from the
extrapolation. It is clear to see from the third image that the
image contrast has been improved by the transmit extrapolation.
[0231] FIG. 14 shows a comparison between an ultrasound image
before and after the implementation of the transmit extrapolation
and a reference image.
[0232] The first image 820 shows a reference image of an apical
4-chamber view of the heart from a different patient to FIG. 13.
The image was formed using 24 diverging waves. Unlike FIG. 13, the
initial reference image for this patient has a poor image
contrast.
[0233] The second image 830 shows an image of the same data set
formed with only 6 diverging waves. As before, the 6 diverging
waves were selected as the central 6 of the 24 steering angles. As
can be seen from the image, the image contrast of the second image
is significantly lower than that of the first image, which in this
case renders a lot of the finer detail extremely unclear.
[0234] The third image 840 shows the result of applying a transmit
extrapolation to the second image as described above. In this case,
an extrapolation filter of order 3 was used to extrapolate the
number of steering angles by a factor of 4. Once again, the image
is generated by coherently compounding the data from the initial 6
transmit angles with the 18 predicted beamsum data from the
extrapolation. It is clear to see from the third image that the
image contrast has been improved by the transmit extrapolation.
[0235] It should be noted that any combination of extrapolation
factor and extrapolation filter order may be used in any of the
methods described above. In addition, any number of steering angles
may be selected in the final method.
[0236] In some ultrasound systems, a combination of the above
methods may be employed in order to further increase the image
quality of the final ultrasound image.
[0237] For example, the aperture extrapolation method, described
with reference to FIGS. 4 to 9, may be performed on a set of
channel data followed by the transmit extrapolation method,
described with reference to FIGS. 10 to 14. In this case, the
aperture extrapolation may be performed for each transmit signal of
the ultrasonic probe and the extrapolated channel data summed over
the aperture, thereby generating a set of aperture extrapolated
channel data. Following the summation, the transmit extrapolation
may be performed on the aperture extrapolated channel data. In this
way, the lateral resolution and image contrast of the final
ultrasound image may be improved. In addition, for PWI and DWI
ultrasound systems, the use of the transmit extrapolation method
may allow for an increase in the frame rate of the ultrasound
image.
[0238] In another example, the transmit extrapolation method may be
performed before the aperture extrapolation method. In this case,
the transmit extrapolation may be performed for each transducer
element of the ultrasonic probe and the extrapolated channel data
summed over all transmit angles, thereby generating a set of
transmit extrapolated channel data. The aperture extrapolation
method may then be performed over the transmit extrapolated channel
data.
[0239] In both cases, the selective attenuation method, described
with reference to FIGS. 2 and 3, may be employed to reduce the
amount of off-axis clutter in the final ultrasound image. As this
method simply attenuates the signals possessing a high spatial
frequency, it may be performed in any order with the aperture
extrapolation and transmit extrapolation methods. Alternatively,
the selective attenuation method may be combined with only the
aperture extrapolation method or the transmit extrapolation
method.
[0240] It should be noted that the signals used to form the
channel, and beam-summed, data may be geometrically aligned prior
to performing the methods described above.
[0241] As discussed above, embodiments make use of a controller for
performing the data processing steps.
[0242] The controller can be implemented in numerous ways, with
software and/or hardware, to perform the various functions
required. A processor is one example of a controller which employs
one or more microprocessors that may be programmed using software
(e.g., microcode) to perform the required functions. A controller
may however be implemented with or without employing a processor,
and also may be implemented as a combination of dedicated hardware
to perform some functions and a processor (e.g., one or more
programmed microprocessors and associated circuitry) to perform
other functions.
[0243] Examples of controller components that may be employed in
various embodiments of the present disclosure include, but are not
limited to, conventional microprocessors, application specific
integrated circuits (ASICs), and field-programmable gate arrays
(FPGAs).
[0244] In various implementations, a processor or controller may be
associated with one or more storage media such as volatile and
non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM.
The storage media may be encoded with one or more programs that,
when executed on one or more processors and/or controllers, perform
at the required functions. Various storage media may be fixed
within a processor or controller or may be transportable, such that
the one or more programs stored thereon can be loaded into a
processor or controller.
[0245] Other variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practicing
the claimed invention, from a study of the drawings, the
disclosure, and the appended claims. In the claims, the word
"comprising" does not exclude other elements or steps, and the
indefinite article "a" or "an" does not exclude a plurality. The
mere fact that certain measures are recited in mutually different
dependent claims does not indicate that a combination of these
measures cannot be used to advantage. Any reference signs in the
claims should not be construed as limiting the scope.
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